Anisotropic octrees: a tool for fast normals estimation on unorganized point clouds
Bibliographic record
Abstract
With the recent advances in remote sensing of objects and environments, point cloud processing has become a\nmajor field of study. Three-dimensional point cloud collected with remote sensing instruments may be very large,\ncontaining up to several tens of billions of points. This imposes the use for efficient and automatic algorithms to\nextract geometric or structural elements of the scanned surfaces. In this paper, we focus on the estimation of normal\ndirections in an unorganized point cloud and provide a curvature indicator. We avoid point-wise operations to accelerate\nthe running time for normals estimation. Instead, our method rely on an innovative anisotropic partitioning\nof the point cloud using an octree structure guided by the geometric complexity of the data and generates patches\nof points. These patches are then approximated by a quadratic surface in order to estimate the normal directions\nand curvatures. Our method has been applied to six models of various types presenting different characteristics and\nperforms, in average, 2.65 times faster than multi-threads implementations available in current pieces of software.\nThe results obtained are a compromise between running time efficiency and normals accuracy. Moreover, this\nwork opens up promising perspectives and can be easily inserted in wide range of workflows.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".